Machine learning in the prediction of depression treatment outcomes: A systematic review and meta-analysis

Mehri Sajjadian, Raymond W. Lam, Roumen Milev, Susan Rotzinger, Benicio N. Frey, Claudio N. Soares, Sagar V. Parikh, Jane A. Foster, Gustavo Turecki, Daniel J. Müller, Stephen C. Strother, Faranak Farzan, Sidney H. Kennedy, Rudolf Uher

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

Background: Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes. Methods: Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment. Results: Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy. Conclusions: The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Original languageEnglish (US)
Pages (from-to)2742-2751
Number of pages10
JournalPsychological Medicine
Volume51
Issue number16
DOIs
StatePublished - Dec 12 2021
Externally publishedYes

Keywords

  • Machine learning
  • MDD
  • meta-analysis
  • predictive analysis
  • systematic review
  • treatment outcome

ASJC Scopus subject areas

  • Applied Psychology
  • Psychiatry and Mental health

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